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Good Booms, Bad Booms
Gary Gorton, Yale and NBER
Guillermo Ordoñez, Penn and NBER
1
Introduction
Financial crises not rare.
- 147 systemic crises since 1970.
- Occur in developed and emerging economies.
- Throughout history of market economies.
Credit booms usually precede financial crises.
We show that credit booms are also not rare.
- Over 50 years, on average a country spends 27 years in a boom,
12 of which were spent in a boom ending in a crisis.
Need macro models that incorporate crises and credit booms.
A crisis is not a “large shock.”
2
Introduction continued
But, some credit booms end in a crisis (bad booms) and other booms
do not (good booms).
Why?
What are the credit booms financing?
We show that: Total Factor Productivity and Labor Productivity
behave differently across good booms and bad booms.
Both booms start with a positive shock to TFP and LP growth. But TFP
and LP growth die off very quickly in bad booms.
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Introduction continued
We present a model consistent with these facts.
In the model, there are credit booms; some end in a crisis, some do
not.
We do not rely on aggregate “shocks.”
But, there is technological change.
The seeds of a crisis are planted many years beforehand, related to
technological change.
Aggregate fluctuations related to low frequency phenomena.
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Technology
Macro looks at high frequency movements, using the H-P filter.
Thinks of growth and cycles as conceptually separate.
RBC models need a negative contemporaneous “shock” to generate a
deviation from trend, a recession.
But, they cannot generate credit booms or crises.
There is a separate literature on TFP and growth (and another on
finance and growth).
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In our sample the average length of a boom is 11 years.
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Good Booms, Bad Booms: Empirical Evidence
Why do some booms end in a crisis while others do not?
We analyze a sample of 34 countries (17 advanced and 17 emerging
markets) over a 50 year span, 1960-2010.
Our credit measure is “domestic credit to the private sector divided by
GDP” (World Bank Macro Dataset).
TFP from Mendoza and Terrones (2012) and LP and other variables
from IMF Financial Statistics.
Crises: Laeven and Valencia (2012).
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Definition of a Credit Boom
Other researchers detrend the credit measure. This defines how long
a boom is since longer booms affect the trend.
We do not want to impose any structure on booms.
We move forward through each country’s credit-GDP and define a
boom to be at least 3 consecutive years of positive annual growth
higher than 5%.
The boom ends whenever we observe at least two consecutive years
of credit growth of zero or less.
Results robust to changes in these thresholds.
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Booms
Average duration of a boom: 11 years.
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Finding 1 (above figures): Significant differences across Good
Booms and Bad Booms. Confirmed in paper.
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Finding 2: Credit growth is the best predictor of crises, but likelihood
mitigated by productivity growth.
Logit(𝐶𝑟𝑖𝑠𝑖𝑠𝑗,𝑡) = Φ(𝛼 + 𝛽Δ𝐶𝑟𝑒𝑑𝑖𝑡𝑗,𝑡−1)
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Finding 3: Credit booms start with a productivity shock.
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Finding 4: Are booms only about credit to households? No.
Repeat analysis with HH Credit. 33 booms of which 17 ended in a
crisis, compared to 87 and 34.
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Finding 5: H-P filtering misses all this.
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Summary
Credit booms are NOT rare and occur in both advanced and
emerging economies.
Booms are 11 years long on average.
Investment booms coincide with credit booms.
Booms start with a positive shock to TFP and LP growth.
But this shock dies out quickly in Bad Booms.
Growth of HH Credit highly correlated with other types of
credit growth.
Results not driven by Financial Crisis of 2007-2008.
These findings are not found when applying H-P filtering.
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Model: Micro Foundations
Financial intermediation is about the provision of short-term debt: money.
Gorton and Pennacchi (1990): banks exist to create information-insensitive debt (riskless) for trading.
- Agents trade; need a security to protect against adverse selection.
- Liquidityinformation-insensitivity; but debt exogenous.
Dang, Gorton, Holmström (2013): debt is the optimal trading security because it is information-insensitive (not just riskless).
- Crisisfear of adverse selection reduces amount traded (and hence welfare). Crisis: info-insensitive->info-sensitive.
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Model
Model is Gorton and Ordonez AER (2014) with two important
extensions (pointed out later).
Model is about bank creation of money, but banks and money
are abstracted from.
Firms Banks Households
Households need money
Banks produce money; collateralized
lending to firms
Receive loans;
Provide collateral
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Firms Banks Households
Households need money
Banks produce money; collateralized
lending to firms
Receive loans;
Provide collateral
Firms Households
Households: collateralized
lending to firms
Receive loans;
Provide collateral
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Model continued
The collateral will be called “land.”
The “money” can be thought of as repo, which is
collateralized. Or, asset-backed commercial paper, etc.
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Model continued
Two overlapping generations every period.
- Young/Households: Endowment and no labor.
- Old/Firms: Labor but no endowment.
Two goods that can be used to consume or produce.
- Numeraire (K): Perishable and reproducible.
- Land (X): Non-perishable and non-reproducible.
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Land Collateral
Land type is unknown without info production.
Learning whether a unit of land if good or bad costs (in terms
of K) γl for lenders to learn and γb for borrowers to learn.
Good land: Generates C units of numeraire (only once).
Bad land: Generates 0 units of numeraire (only once).
Each unit of land has a common belief p of being good.
X = {C with probability p0 with probability (1 − p)
.
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Firms that have land with a high enough probability that it is
good collateral, p, can raise funds in the loan market and
produce. We call them active firms.
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Firms
Continuum of mass 1 of risk neutral individuals/firms (old generation).
When old each has entrepreneurial ideas L* (no disutility) and no K.
A firm is a combination of labor, L*, a unit of land X, and numeraire K
(“capital”), to produce more numeraire:
Y = {A min{K, L} with probability q
0 with probability (1 − q)
where A>1.
Firms need to borrow K to produce. Optimal K*=L*.
Production is efficient, i.e., qA>1.
Y and q are nonverifiable.
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Technology
There are two types of projects available.
A fraction ψ has a high probability of success, qH, and the rest have a
low probability of success qL.
All projects are efficient: qHA>qLA>1, which implies that the optimal
scale of production is K*=L*.
A production opportunity set is defined by ψ.
Ψ arrives (exogenously). A different ψ may arrive later (but not in the
model).
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Before approaching households for a loan, active firms are randomly
assigned to a queue to choose their project. When it is their turn to
pick, they pick the best available project.
So, the firm privately knows the quality, q, of its project.
Let η be the mass of active firms in the economy.
Lenders beliefs about the probability of success of any given firm are:
�̂�(𝜂) = {
𝑞𝐻 𝑖𝑓 𝜂 < 𝜓𝜓
𝜂𝑞𝐻 + (1 −
𝜓
𝜂)𝑞𝐿 𝑖𝑓 𝜂≥𝜓
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𝜂 is a function of time: 𝜂𝑡. In the credit boom, more and more firms
will become active.
Consequently, the average productivity of projects in the economy,
�̂�(𝜂), which is also the lenders’ beliefs about the probability of
success of a given firm, weakly declines with the mass of active firms,
η, and reaches a minimum when all firms are active, i.e., η=1.
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Households
Continuum of mass 1 of risk neutral households (young generation).
Each is born endowed with 𝐾 > 𝐾∗ of numeraire good and no L*.
They can lend K to firms and buy land X from firms.
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Market for land
At the end of a period:
- Match of a household with a firm (young with old).
- Negotiation power to the buyer (take-it-or-leave it offer).
- Price of land is pC.
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Lending market
At the beginning of the period:
- The output of firms is non-contractible.
- Firms can post a fraction x of land as collateral.
- Match of a household and a firm.
- Negotiation power to the borrower.
- Assume C>K*.
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Information-Sensitive (IS) Debt
If information is produced then firms and lenders learn the true value
of the collateral.
If (risk neutral and competitive) lenders are producing information
then:
𝑝(�̂�(𝜂)𝑅𝐼𝑆𝑙 +(1-�̂�(𝜂))𝑥𝐼𝑆
𝑙 𝐶 − 𝐾) = 𝛾𝑙
Where: K is the loan size, 𝑅𝐼𝑆𝑙 is the face value of the debt and 𝑥𝐼𝑆
𝑙 is
the fraction of land posted as collateral.
Note that if 𝑅𝐼𝑆𝑙 > 𝑥𝐼𝑆
𝑙 𝐶, the firm will always default, handing over
collateral rather than repay debt.
If 𝑅𝐼𝑆𝑙 < 𝑥𝐼𝑆
𝑙 𝐶, then the firm will always sell the collateral at price 𝐶
and repay 𝑅𝐼𝑆𝑙 .
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So, 𝑅𝐼𝑆𝑙 = 𝑥𝐼𝑆
𝑙 𝐶 𝑥𝐼𝑆𝑙 =
𝑝𝐾+𝛾𝑙
𝑝𝐶≤ 1.
Note that, since the interest rates and the fraction of collateral posted
do not depend on q—because the debt is risk-free firms cannot
signal their q by offering to pay different interest rates.
This leads to expected profits:
𝐸(𝜋|𝑝, 𝑞, 𝐼𝑆, 𝑙) = max {𝑝𝐾∗(𝑞𝐴 − 1) − 𝛾𝑙 , 0}
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If 𝛾𝑏 < 𝛾𝑙, then firms choose to produce information. Then expected
profits are:
𝐸(𝜋|𝑝, 𝑞, 𝐼𝑆) = max {𝑝𝐾∗(𝑞𝐴 − 1) − min {𝛾𝑏(𝑞𝐴 − 1), 𝛾𝑙},0} (4)
Since the opportunity cost for firms to produce information
is 𝛾𝑏(𝑞𝐴 − 1).
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Information-Insensitive (II) Debt
Neither firms nor lenders produce information about the value of the
collateral.
Lenders set 𝑅𝐼𝐼 and x to break even:
�̂�(𝜂)𝑅𝐼𝐼 + (1 − �̂�(𝜂))𝑝𝑥𝐼𝐼𝐶 = 𝐾
Subject to 𝑅𝐼𝐼 = 𝑝𝑥𝐼𝐼𝐶. Then 𝑥𝐼𝐼 =𝐾
𝑝𝐶≤ 1.
Lenders want to deviate and produce information if expected gains
are greater than losses:
𝑝(�̂�(𝜂)𝑅𝐼𝐼 + (1 − �̂�(𝜂))𝑥𝐼𝐼𝐶 − 𝐾) > 𝛾𝑙 ⇒ (1 − 𝑝)(1 − �̂�(𝜂))𝐾 > 𝛾𝑙.
Lenders do not want to deviate if:
𝐾 <𝛾𝑙
(1−𝑝)(1−�̂�(𝜼)). (5)
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From the point of view of the lenders, info-insensitive loans are such
that:
𝐾 < 𝐾𝑙(𝑝|�̂�(𝜂), 𝐼𝐼) = 𝑚𝑖𝑛{𝐾∗, 𝛾𝑙
(1−𝑝)(1−�̂�(𝜂)), 𝑝𝐶} **
The condition that guarantees that borrowers do not want to produce
info is:
𝑝(𝐾∗ − 𝛾𝑏)(𝑞𝐴 − 1) + (1 − 𝑝) min{𝐾, 𝐾∗ − 𝛾𝑏} (𝑞𝐴 − 1) < 𝐾(𝑞𝐴 − 1)
Or, in terms of loan size: 𝐾 > 𝐾𝑏(𝑝|�̂�(𝜂), 𝐼𝐼) ≡ 𝐾∗ − 𝛾𝑏 ##
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Combining ** and ## defines 𝑝∗such that 𝐾𝑙(𝑝∗) = 𝐾𝑏(𝑝∗) ---
depends on �̂�(𝜂).
I.e. info-insensitive debt is feasible only when the loan is: (1) above the
red dotted line, and (2) below the solid blue line.
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The cutoff 𝑝𝐻is the belief under which firms reduce borrowing, less
than the optimal 𝐾∗, to prevent info production, from eq. (5):
𝑝𝐻 = 1 −𝛾𝑙
𝐾∗(1 − �̂�(𝜂))
Note that 𝑝𝐻 is inversely related to �̂�(𝜂). As there are more active
firms, 𝑝𝐻 increases (moves to the right).
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Dynamics
Evolution of collateral value:
Each collateral is associated with one of three possible beliefs:
- 𝑝 = 0, if info produced and collateral is bad.
- 𝑝 = 1, if info produced and collateral is good.
- 𝑝 = �̂�, if no information was produced.
Assume that at t=0 all collateral qualities are known.
λ
1-λ
Collateral value remains unchanged.
Idiosyncratic shock: Collateral value
changes, becomes good with probability �̂�
.
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Define 𝜒 ≡ 𝜆�̂� + (1 − 𝜆). This is the fraction of active firms after
idiosyncratic shocks in a single period.
A fraction (1 − 𝜆) of all collateral suffers the shock and their
perceived quality, absent info production, is �̂� while a fraction 𝜆 of
the collateral known to be good, �̂�, remains good.
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Prop 1: 𝜒 such that 𝑝𝐹𝑙 (�̂�(𝜒)) < �̂� < 𝑝𝐹
ℎ(�̂�(𝜒)). Then stuck in the
IS range. Info is produced every period. Consumption is low.
Prop 2: Crisis.
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Recall that the cutoff 𝑝∗(�̂�(𝜂)) is monotonically increasing as �̂�(𝜂)
declines. Moving to the right in the figure.
More active firms reduces the perceived average quality of any
single firm.
Prop 2: �̂�(𝜒) is such that �̂� > 𝑝∗(�̂�(𝜂)) and �̂� < 𝑝∗(�̂�(1)). Then
Information Cycles.
Intuition: Starting from a date when there is perfect information,
such that there is no incentive to acquire information. At the start
the quality of the projects is high. But, then info decays over time
more and more firms get loans (credit boom), but the average
quality of the projects declines. At some point there is a switch to
producing information.
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Empirical Tests
During Bad Booms firms are increasingly likely to default. The
economy is increasingly fragile.
Fragility: Atkeson, Eisfeldt and Olivier-Weill (2013): 1/vol.
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𝐿𝑜𝑔𝑖𝑡(𝐵𝑎𝑑𝐵𝑜𝑜𝑚𝑗,𝑡|𝐵𝑜𝑜𝑚𝑗,𝑡) = Φ(𝛼 +1
𝑣𝑜𝑙𝑗,𝑡−1)
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TFP should be related to fragility.
Δ(𝑇𝐹𝑃)𝑗,𝑡 = 𝛼 + 𝛽Δ1
𝑣𝑜𝑙𝑗,𝑡−1+ 𝜀𝑗,𝑡
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Conclusion
Current macro models rely heavily on “technology” shocks. But,
they cannot explain credit booms or crises.
Dynamics of “regular” cycles and systemic events? Technological
change is important for boom, recessions, and crises— possibly in
one unified model.
We do not rely on an exogenous “shock.” A crisis is not a big
“shock.”
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Preview of “Aggregate Information Dynamics” (with Chousakos
and Ordonez)
Recessions/Good times defined differently.
The amount of information in the economy varies over time.
o Information measures based on stock prices.
More information is produced going into a recession with a crisis.
There is some feedback effect to investment.
o Investment moves from lowest quartile of firms to next highest
quartile (sorted by Tobin’s Q).